Overview

Brought to you by YData

Dataset statistics

Number of variables11
Number of observations699
Missing cells0
Missing cells (%)0.0%
Duplicate rows9
Duplicate rows (%)1.3%
Total size in memory60.2 KiB
Average record size in memory88.2 B

Variable types

Numeric10
Categorical1

Alerts

Dataset has 9 (1.3%) duplicate rowsDuplicates
Bare_Nuclei is highly overall correlated with Bland_Chromatin and 7 other fieldsHigh correlation
Bland_Chromatin is highly overall correlated with Bare_Nuclei and 7 other fieldsHigh correlation
Class is highly overall correlated with Bare_Nuclei and 8 other fieldsHigh correlation
Clump_Thickness is highly overall correlated with Bare_Nuclei and 7 other fieldsHigh correlation
Marginal_Adhesion is highly overall correlated with Bare_Nuclei and 7 other fieldsHigh correlation
Mitoses is highly overall correlated with Class and 2 other fieldsHigh correlation
Normal_Nucleoli is highly overall correlated with Bare_Nuclei and 8 other fieldsHigh correlation
Single_Epithelial_Cell_Size is highly overall correlated with Bare_Nuclei and 7 other fieldsHigh correlation
Uniformity_of_Cell_Shape is highly overall correlated with Bare_Nuclei and 7 other fieldsHigh correlation
Uniformity_of_Cell_Size is highly overall correlated with Bare_Nuclei and 8 other fieldsHigh correlation

Reproduction

Analysis started2024-11-17 13:25:27.438563
Analysis finished2024-11-17 13:25:33.612205
Duration6.17 seconds
Software versionydata-profiling vv4.12.0
Download configurationconfig.json

Variables

Sample_Code_Number
Real number (ℝ)

Distinct645
Distinct (%)92.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1071704.1
Minimum61634
Maximum13454352
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2024-11-17T18:55:33.673356image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum61634
5-th percentile411453
Q1870688.5
median1171710
Q31238298
95-th percentile1333890.8
Maximum13454352
Range13392718
Interquartile range (IQR)367609.5

Descriptive statistics

Standard deviation617095.73
Coefficient of variation (CV)0.57580794
Kurtosis257.71716
Mean1071704.1
Median Absolute Deviation (MAD)104381
Skewness13.675326
Sum7.4912116 × 108
Variance3.8080714 × 1011
MonotonicityNot monotonic
2024-11-17T18:55:33.759320image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1182404 6
 
0.9%
1276091 5
 
0.7%
1198641 3
 
0.4%
897471 2
 
0.3%
1116192 2
 
0.3%
385103 2
 
0.3%
411453 2
 
0.3%
1293439 2
 
0.3%
1143978 2
 
0.3%
560680 2
 
0.3%
Other values (635) 671
96.0%
ValueCountFrequency (%)
61634 1
0.1%
63375 1
0.1%
76389 1
0.1%
95719 1
0.1%
128059 1
0.1%
142932 1
0.1%
144888 1
0.1%
145447 1
0.1%
160296 1
0.1%
167528 1
0.1%
ValueCountFrequency (%)
13454352 1
0.1%
8233704 1
0.1%
1371920 1
0.1%
1371026 1
0.1%
1369821 1
0.1%
1368882 1
0.1%
1368273 1
0.1%
1368267 1
0.1%
1365328 1
0.1%
1365075 1
0.1%

Clump_Thickness
Real number (ℝ)

High correlation 

Distinct10
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.4177396
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2024-11-17T18:55:33.830085image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q36
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.8157407
Coefficient of variation (CV)0.63737135
Kurtosis-0.62371541
Mean4.4177396
Median Absolute Deviation (MAD)2
Skewness0.59285853
Sum3088
Variance7.9283955
MonotonicityNot monotonic
2024-11-17T18:55:33.890720image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 145
20.7%
5 130
18.6%
3 108
15.5%
4 80
11.4%
10 69
9.9%
2 50
 
7.2%
8 46
 
6.6%
6 34
 
4.9%
7 23
 
3.3%
9 14
 
2.0%
ValueCountFrequency (%)
1 145
20.7%
2 50
 
7.2%
3 108
15.5%
4 80
11.4%
5 130
18.6%
6 34
 
4.9%
7 23
 
3.3%
8 46
 
6.6%
9 14
 
2.0%
10 69
9.9%
ValueCountFrequency (%)
10 69
9.9%
9 14
 
2.0%
8 46
 
6.6%
7 23
 
3.3%
6 34
 
4.9%
5 130
18.6%
4 80
11.4%
3 108
15.5%
2 50
 
7.2%
1 145
20.7%

Uniformity_of_Cell_Size
Real number (ℝ)

High correlation 

Distinct10
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.1344778
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2024-11-17T18:55:33.950975image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q35
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.0514591
Coefficient of variation (CV)0.97351434
Kurtosis0.098802885
Mean3.1344778
Median Absolute Deviation (MAD)0
Skewness1.2331366
Sum2191
Variance9.3114027
MonotonicityNot monotonic
2024-11-17T18:55:34.008991image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 384
54.9%
10 67
 
9.6%
3 52
 
7.4%
2 45
 
6.4%
4 40
 
5.7%
5 30
 
4.3%
8 29
 
4.1%
6 27
 
3.9%
7 19
 
2.7%
9 6
 
0.9%
ValueCountFrequency (%)
1 384
54.9%
2 45
 
6.4%
3 52
 
7.4%
4 40
 
5.7%
5 30
 
4.3%
6 27
 
3.9%
7 19
 
2.7%
8 29
 
4.1%
9 6
 
0.9%
10 67
 
9.6%
ValueCountFrequency (%)
10 67
 
9.6%
9 6
 
0.9%
8 29
 
4.1%
7 19
 
2.7%
6 27
 
3.9%
5 30
 
4.3%
4 40
 
5.7%
3 52
 
7.4%
2 45
 
6.4%
1 384
54.9%

Uniformity_of_Cell_Shape
Real number (ℝ)

High correlation 

Distinct10
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2074392
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2024-11-17T18:55:34.066768image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q35
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.9719128
Coefficient of variation (CV)0.9265687
Kurtosis0.00701098
Mean3.2074392
Median Absolute Deviation (MAD)0
Skewness1.1618592
Sum2242
Variance8.8322655
MonotonicityNot monotonic
2024-11-17T18:55:34.126048image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 353
50.5%
2 59
 
8.4%
10 58
 
8.3%
3 56
 
8.0%
4 44
 
6.3%
5 34
 
4.9%
6 30
 
4.3%
7 30
 
4.3%
8 28
 
4.0%
9 7
 
1.0%
ValueCountFrequency (%)
1 353
50.5%
2 59
 
8.4%
3 56
 
8.0%
4 44
 
6.3%
5 34
 
4.9%
6 30
 
4.3%
7 30
 
4.3%
8 28
 
4.0%
9 7
 
1.0%
10 58
 
8.3%
ValueCountFrequency (%)
10 58
 
8.3%
9 7
 
1.0%
8 28
 
4.0%
7 30
 
4.3%
6 30
 
4.3%
5 34
 
4.9%
4 44
 
6.3%
3 56
 
8.0%
2 59
 
8.4%
1 353
50.5%

Marginal_Adhesion
Real number (ℝ)

High correlation 

Distinct10
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.806867
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2024-11-17T18:55:34.185881image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q34
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.8553792
Coefficient of variation (CV)1.0172834
Kurtosis0.98794707
Mean2.806867
Median Absolute Deviation (MAD)0
Skewness1.5244681
Sum1962
Variance8.1531906
MonotonicityNot monotonic
2024-11-17T18:55:34.244675image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 407
58.2%
3 58
 
8.3%
2 58
 
8.3%
10 55
 
7.9%
4 33
 
4.7%
8 25
 
3.6%
5 23
 
3.3%
6 22
 
3.1%
7 13
 
1.9%
9 5
 
0.7%
ValueCountFrequency (%)
1 407
58.2%
2 58
 
8.3%
3 58
 
8.3%
4 33
 
4.7%
5 23
 
3.3%
6 22
 
3.1%
7 13
 
1.9%
8 25
 
3.6%
9 5
 
0.7%
10 55
 
7.9%
ValueCountFrequency (%)
10 55
 
7.9%
9 5
 
0.7%
8 25
 
3.6%
7 13
 
1.9%
6 22
 
3.1%
5 23
 
3.3%
4 33
 
4.7%
3 58
 
8.3%
2 58
 
8.3%
1 407
58.2%

Single_Epithelial_Cell_Size
Real number (ℝ)

High correlation 

Distinct10
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2160229
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2024-11-17T18:55:34.304148image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q34
95-th percentile8
Maximum10
Range9
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.2142999
Coefficient of variation (CV)0.68852118
Kurtosis2.1690664
Mean3.2160229
Median Absolute Deviation (MAD)0
Skewness1.7121718
Sum2248
Variance4.903124
MonotonicityNot monotonic
2024-11-17T18:55:34.362709image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
2 386
55.2%
3 72
 
10.3%
4 48
 
6.9%
1 47
 
6.7%
6 41
 
5.9%
5 39
 
5.6%
10 31
 
4.4%
8 21
 
3.0%
7 12
 
1.7%
9 2
 
0.3%
ValueCountFrequency (%)
1 47
 
6.7%
2 386
55.2%
3 72
 
10.3%
4 48
 
6.9%
5 39
 
5.6%
6 41
 
5.9%
7 12
 
1.7%
8 21
 
3.0%
9 2
 
0.3%
10 31
 
4.4%
ValueCountFrequency (%)
10 31
 
4.4%
9 2
 
0.3%
8 21
 
3.0%
7 12
 
1.7%
6 41
 
5.9%
5 39
 
5.6%
4 48
 
6.9%
3 72
 
10.3%
2 386
55.2%
1 47
 
6.7%

Bare_Nuclei
Real number (ℝ)

High correlation 

Distinct10
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4864092
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2024-11-17T18:55:34.421116image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q35
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.6219288
Coefficient of variation (CV)1.0388708
Kurtosis-0.72646662
Mean3.4864092
Median Absolute Deviation (MAD)0
Skewness1.0253473
Sum2437
Variance13.118368
MonotonicityNot monotonic
2024-11-17T18:55:34.480665image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 418
59.8%
10 132
 
18.9%
2 30
 
4.3%
5 30
 
4.3%
3 28
 
4.0%
8 21
 
3.0%
4 19
 
2.7%
9 9
 
1.3%
7 8
 
1.1%
6 4
 
0.6%
ValueCountFrequency (%)
1 418
59.8%
2 30
 
4.3%
3 28
 
4.0%
4 19
 
2.7%
5 30
 
4.3%
6 4
 
0.6%
7 8
 
1.1%
8 21
 
3.0%
9 9
 
1.3%
10 132
 
18.9%
ValueCountFrequency (%)
10 132
 
18.9%
9 9
 
1.3%
8 21
 
3.0%
7 8
 
1.1%
6 4
 
0.6%
5 30
 
4.3%
4 19
 
2.7%
3 28
 
4.0%
2 30
 
4.3%
1 418
59.8%

Bland_Chromatin
Real number (ℝ)

High correlation 

Distinct10
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4377682
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2024-11-17T18:55:34.619992image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q35
95-th percentile8
Maximum10
Range9
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.4383643
Coefficient of variation (CV)0.70928698
Kurtosis0.18462131
Mean3.4377682
Median Absolute Deviation (MAD)1
Skewness1.0999691
Sum2403
Variance5.9456202
MonotonicityNot monotonic
2024-11-17T18:55:34.678762image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
2 166
23.7%
3 165
23.6%
1 152
21.7%
7 73
10.4%
4 40
 
5.7%
5 34
 
4.9%
8 28
 
4.0%
10 20
 
2.9%
9 11
 
1.6%
6 10
 
1.4%
ValueCountFrequency (%)
1 152
21.7%
2 166
23.7%
3 165
23.6%
4 40
 
5.7%
5 34
 
4.9%
6 10
 
1.4%
7 73
10.4%
8 28
 
4.0%
9 11
 
1.6%
10 20
 
2.9%
ValueCountFrequency (%)
10 20
 
2.9%
9 11
 
1.6%
8 28
 
4.0%
7 73
10.4%
6 10
 
1.4%
5 34
 
4.9%
4 40
 
5.7%
3 165
23.6%
2 166
23.7%
1 152
21.7%

Normal_Nucleoli
Real number (ℝ)

High correlation 

Distinct10
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.8669528
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2024-11-17T18:55:34.738333image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q34
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.0536339
Coefficient of variation (CV)1.0651148
Kurtosis0.47426868
Mean2.8669528
Median Absolute Deviation (MAD)0
Skewness1.4222613
Sum2004
Variance9.32468
MonotonicityNot monotonic
2024-11-17T18:55:34.795700image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 443
63.4%
10 61
 
8.7%
3 44
 
6.3%
2 36
 
5.2%
8 24
 
3.4%
6 22
 
3.1%
5 19
 
2.7%
4 18
 
2.6%
7 16
 
2.3%
9 16
 
2.3%
ValueCountFrequency (%)
1 443
63.4%
2 36
 
5.2%
3 44
 
6.3%
4 18
 
2.6%
5 19
 
2.7%
6 22
 
3.1%
7 16
 
2.3%
8 24
 
3.4%
9 16
 
2.3%
10 61
 
8.7%
ValueCountFrequency (%)
10 61
 
8.7%
9 16
 
2.3%
8 24
 
3.4%
7 16
 
2.3%
6 22
 
3.1%
5 19
 
2.7%
4 18
 
2.6%
3 44
 
6.3%
2 36
 
5.2%
1 443
63.4%

Mitoses
Real number (ℝ)

High correlation 

Distinct9
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5894134
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2024-11-17T18:55:34.854236image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile5
Maximum10
Range9
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.7150779
Coefficient of variation (CV)1.0790634
Kurtosis12.657878
Mean1.5894134
Median Absolute Deviation (MAD)0
Skewness3.5606578
Sum1111
Variance2.9414923
MonotonicityNot monotonic
2024-11-17T18:55:34.914265image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1 579
82.8%
2 35
 
5.0%
3 33
 
4.7%
10 14
 
2.0%
4 12
 
1.7%
7 9
 
1.3%
8 8
 
1.1%
5 6
 
0.9%
6 3
 
0.4%
ValueCountFrequency (%)
1 579
82.8%
2 35
 
5.0%
3 33
 
4.7%
4 12
 
1.7%
5 6
 
0.9%
6 3
 
0.4%
7 9
 
1.3%
8 8
 
1.1%
10 14
 
2.0%
ValueCountFrequency (%)
10 14
 
2.0%
8 8
 
1.1%
7 9
 
1.3%
6 3
 
0.4%
5 6
 
0.9%
4 12
 
1.7%
3 33
 
4.7%
2 35
 
5.0%
1 579
82.8%

Class
Categorical

High correlation 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size39.7 KiB
2
458 
4
241 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters699
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 458
65.5%
4 241
34.5%

Length

2024-11-17T18:55:34.983425image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-17T18:55:35.044490image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
2 458
65.5%
4 241
34.5%

Most occurring characters

ValueCountFrequency (%)
2 458
65.5%
4 241
34.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 699
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 458
65.5%
4 241
34.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 699
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 458
65.5%
4 241
34.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 699
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 458
65.5%
4 241
34.5%

Interactions

2024-11-17T18:55:32.859663image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-11-17T18:55:27.622539image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-11-17T18:55:28.274465image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-11-17T18:55:28.895797image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-11-17T18:55:29.446267image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-11-17T18:55:30.002923image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-11-17T18:55:30.554798image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-11-17T18:55:31.113848image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-11-17T18:55:31.661382image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-11-17T18:55:32.295551image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-11-17T18:55:32.923598image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-11-17T18:55:27.696547image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-11-17T18:55:28.339335image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-11-17T18:55:28.954229image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-11-17T18:55:29.510094image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-11-17T18:55:30.064481image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-11-17T18:55:30.618085image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-11-17T18:55:31.177134image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-11-17T18:55:31.727040image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-11-17T18:55:32.358739image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-11-17T18:55:32.977638image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-11-17T18:55:27.763409image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-11-17T18:55:28.392975image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-11-17T18:55:29.009483image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-11-17T18:55:29.565501image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-11-17T18:55:30.119860image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-11-17T18:55:30.672093image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-11-17T18:55:31.231466image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-11-17T18:55:31.780697image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-11-17T18:55:32.412268image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-11-17T18:55:33.032499image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-11-17T18:55:27.824998image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-11-17T18:55:28.446795image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-11-17T18:55:29.063135image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-11-17T18:55:29.618989image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-11-17T18:55:30.175451image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-11-17T18:55:30.726097image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-11-17T18:55:31.285939image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-11-17T18:55:31.834742image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-11-17T18:55:32.469551image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-11-17T18:55:33.086145image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-11-17T18:55:27.883689image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-11-17T18:55:28.501405image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-11-17T18:55:29.118057image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-11-17T18:55:29.674187image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-11-17T18:55:30.230619image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-11-17T18:55:30.782972image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-11-17T18:55:31.339965image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-11-17T18:55:31.974071image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-11-17T18:55:32.522400image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-11-17T18:55:33.140220image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-11-17T18:55:27.944757image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-11-17T18:55:28.556900image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-11-17T18:55:29.173068image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-11-17T18:55:29.728979image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-11-17T18:55:30.283658image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-11-17T18:55:30.836782image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-11-17T18:55:31.393134image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-11-17T18:55:32.028327image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-11-17T18:55:32.576113image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-11-17T18:55:33.195907image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-11-17T18:55:28.009570image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-11-17T18:55:28.612309image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-11-17T18:55:29.228280image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-11-17T18:55:29.782879image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-11-17T18:55:30.338357image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-11-17T18:55:30.892131image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-11-17T18:55:31.448948image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-11-17T18:55:32.082328image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-11-17T18:55:32.630612image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-11-17T18:55:33.248541image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-11-17T18:55:28.075575image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-11-17T18:55:28.665662image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-11-17T18:55:29.283364image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-11-17T18:55:29.839019image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-11-17T18:55:30.393521image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-11-17T18:55:30.946557image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-11-17T18:55:31.501700image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-11-17T18:55:32.135902image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-11-17T18:55:32.682877image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-11-17T18:55:33.305103image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-11-17T18:55:28.145000image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-11-17T18:55:28.719782image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-11-17T18:55:29.338126image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-11-17T18:55:29.893583image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-11-17T18:55:30.446903image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-11-17T18:55:31.001933image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-11-17T18:55:31.555998image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-11-17T18:55:32.188864image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-11-17T18:55:32.738359image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-11-17T18:55:33.359217image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-11-17T18:55:28.209551image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-11-17T18:55:28.774077image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-11-17T18:55:29.390926image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-11-17T18:55:29.949852image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-11-17T18:55:30.501493image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-11-17T18:55:31.058331image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-11-17T18:55:31.610200image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-11-17T18:55:32.243510image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-11-17T18:55:32.791280image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Correlations

2024-11-17T18:55:35.090189image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Bare_NucleiBland_ChromatinClassClump_ThicknessMarginal_AdhesionMitosesNormal_NucleoliSample_Code_NumberSingle_Epithelial_Cell_SizeUniformity_of_Cell_ShapeUniformity_of_Cell_Size
Bare_Nuclei1.0000.6690.8350.5860.6940.4780.649-0.1190.6890.7410.761
Bland_Chromatin0.6691.0000.8040.5380.6250.3870.662-0.0960.6400.6920.719
Class0.8350.8041.0000.7380.7380.5190.7680.0000.7910.8600.875
Clump_Thickness0.5860.5380.7381.0000.5420.4190.570-0.0040.5840.6640.666
Marginal_Adhesion0.6940.6250.7380.5421.0000.4470.634-0.0500.6680.7120.743
Mitoses0.4780.3870.5190.4190.4471.0000.504-0.0750.4800.4730.509
Normal_Nucleoli0.6490.6620.7680.5700.6340.5041.000-0.0710.7060.7250.757
Sample_Code_Number-0.119-0.0960.000-0.004-0.050-0.075-0.0711.000-0.087-0.060-0.043
Single_Epithelial_Cell_Size0.6890.6400.7910.5840.6680.4800.706-0.0871.0000.7590.787
Uniformity_of_Cell_Shape0.7410.6920.8600.6640.7120.4730.725-0.0600.7591.0000.892
Uniformity_of_Cell_Size0.7610.7190.8750.6660.7430.5090.757-0.0430.7870.8921.000

Missing values

2024-11-17T18:55:33.435384image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
A simple visualization of nullity by column.
2024-11-17T18:55:33.542563image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Sample_Code_NumberClump_ThicknessUniformity_of_Cell_SizeUniformity_of_Cell_ShapeMarginal_AdhesionSingle_Epithelial_Cell_SizeBare_NucleiBland_ChromatinNormal_NucleoliMitosesClass
01000025511121.03112
110029455445710.03212
21015425311122.03112
31016277688134.03712
41017023411321.03112
51017122810108710.09714
610180991111210.03112
71018561212121.03112
81033078211121.01152
91033078421121.02112
Sample_Code_NumberClump_ThicknessUniformity_of_Cell_SizeUniformity_of_Cell_ShapeMarginal_AdhesionSingle_Epithelial_Cell_SizeBare_NucleiBland_ChromatinNormal_NucleoliMitosesClass
689654546111121.01182
690654546111321.01112
69169509151010545.04414
692714039311121.01112
693763235311121.02122
694776715311132.01112
695841769211121.01112
69688882051010373.081024
697897471486434.010614
698897471488545.010414

Duplicate rows

Most frequently occurring

Sample_Code_NumberClump_ThicknessUniformity_of_Cell_SizeUniformity_of_Cell_ShapeMarginal_AdhesionSingle_Epithelial_Cell_SizeBare_NucleiBland_ChromatinNormal_NucleoliMitosesClass# duplicates
03206753352310.071142
1466906111121.011122
2704097111111.021122
3733639311121.031122
41100524610102810.073342
51116116910101108.033142
61198641311121.031122
71218860111111.031122
81321942511121.031122